In pattern classifier devices, an algorithm of AdaBoost is known in which a cascade connection of a plurality of weak classifiers forms a single classifier. Hereinafter, a connection of a plurality of weak classifiers will be defined as a single classifier (also referred to as strong classifier). AdaBoost is often used as an effective approach for determining a face region in an image. In AdaBoost, it is necessary to separately prepare, in advance, subclass classifiers for the front direction, the left direction, and the right direction in order to response to changes according to subclasses for the front direction, the left direction, and the right direction, for example, and to apply all the subclass classifiers to an input pattern.
The conventional technique, however, is disadvantageous in that since a single subclass decided first is used to execute subsequent determination processing, the performance depends to a large extent on a first decision rule, and that the accuracy of determination processing is reduced if the rule is not appropriately designed.